Location Decision of Logistics Distribution Centers Based on Artificial Neural Network

Nie, Jinting

doi:10.14704/nq.2018.16.6.1613

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…It has demonstrated appealing performance in prediction and evaluation in complex decision-making situations [21]. Nie [22] applied BP neural network algorithm to investigate the site selection and evaluation of logistic centers. The site location and evaluation of logistic centers established according to the BP neural network could objectively reflect the mapping relationship between the distribution capacity of the logistics center and the key elements affecting site selection.…”

Section: Related Workmentioning

confidence: 99%

Comprehensive Evaluation Model for Resettlement Site Selection in Karst Areas Using Multiobjective Particle Swarm Optimization

Qin

Sun

et al. 2021

IEEE Access

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Resettlement site selection is a systematic project constrained by multiple geographic, social, and economic factors, and site selection in karst mountainous areas is even more complicated. The key step in resettlement site selection is to reduce the uncertainty and inaccuracy of evaluation models. This study constructed an evaluation index system of a model for resettlement site selection in karst mountainous areas from multiple aspects, including karst rocky desertification environment, degree of geological hazard development, resource and environmental carrying capacity, transportation and economic development potential, as well as relocation cost. A hierarchical model was applied to classify the indices, determine the weighted optimal solution of the eigenvectors in matrices, and evaluate the suitability. A new algorithm for model parameter inference was developed based on four objectives, including achieving suitable ecoenvironment capacity, ensuring production and development, controlling relocation cost, and meeting the comprehensive requirements. The objective functions and constraints of the model with four objectives were also established. To address this problem, the multiobjective particle swarm algorithm was applied, and a novel concept of "noninferior solution set" was proposed. The objective functions and constraint conditions of the algorithm were optimized and improved from four aspects: parameter setting, representativeness of particles, noninferior solution set storage, and selection of global noninferior solution set. Guanling County, an area in Guizhou Province with a typical development of karst rocky desertification, was selected as the study site. The latest data for land use mapping were adopted as the smallest evaluation unit, and the improved multiobjective particle swarm algorithm was employed in evaluating the resettlement site selection within this area. The improved algorithm was applicable to a wide range of multiobjective optimization problems.

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Section: Related Workmentioning

confidence: 99%

Comprehensive Evaluation Model for Resettlement Site Selection in Karst Areas Using Multiobjective Particle Swarm Optimization

Qin

Sun

et al. 2021

IEEE Access

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“…With the popularity of the Internet and mobile terminals, e-commerce has developed rapidly. At the same time, people's living standards have also improved, and the demand for food begin to diversify with the development of the economy [1]. Fresh fruits and vegetables, as well as seafood, which require transportation with cryopreservation, have gradually become part of goods in e-commerce.…”

Section: Introductionmentioning

confidence: 99%

Urban cold-chain logistics demand predicting model based on improved neural network model

Chen

Shao

2020

Int. J. Metrol. Qual. Eng.

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With the popularity of the Internet and mobile terminals, the development of e-commerce has become hotter. Therefore, e-commerce research starts to focus on the statistics and prediction of the cargo volume of logistics. This study briefly introduced the back-propagation (BP) neural network model and principal component analysis (PCA) method and combined them to obtain an improved PCA-BP neural network model. Then the traditional BP neural network model and the improved PCA-BP neural network model were used to perform the empirical analysis of the cold chain logistics demand of fruits and vegetables in city A from 2010 to 2018. The results showed that the main factors that affected the local cold chain logistics demand were the growth rate of GDP, the added value of primary industry, the planting area of fruits and vegetables, and the consumption price index of fruits and vegetables; both kinds of neural networks model could effectively predict the cold chain logistics demand, but the predicted value of the PCA-BP neural network model was more fitted with the actual value. The prediction error of the BP neural network model was larger, and the fluctuation was obvious within the prediction interval. Moreover, the time required for the prediction by the PCA-BP neural network model was less than that by the BP neural network model. In summary, the improved PCA-BP neural network model is faster and more accurate than the traditional BP model in predicting the cold chain logistics demand.

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Optimization of an Urban Microgreen Space Distribution Based on the PS-ACO Algorithm: A Case Study of Shenyang, China

Chen,

Dong,

Wang

et al. 2024

Land

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In this study, optimization of the microgreen space distribution through multistage regulation is investigated, with the goal of alleviating the imbalance between the supply and demand of green resources in the central urban area of Shenyang. An optimized evaluation model of green space supply and demand is employed to calculate the green space accessibility index at a 100-meter grid scale and identify different levels of green space resource supply and demand. Priority is given to supplementing resources for the elderly population by balancing the green space supply in vulnerable areas. Particle swarm—ant colony optimization (PS-ACO) is used to select microgreen space sites within each priority level. On the basis of the “important-urgent” quadrant analysis, S1-priority residential areas account for 8.12% of the grid, S2-priority areas account for 67.01%, and S3-priority areas account for 24.87%. The PS-ACO algorithm outputs potential microgreen space sites within each priority level to accurately regulate the green space distribution in residential areas with different supply pressures and limited land availability. A spatial correlation analysis of the new sites reveals good spatial dispersion within service units, effectively alleviating demand pressures, and good aggregation at a regional scale to address imbalances in the supply of green space in a targeted manner. Thus, the optimized results of the PS-ACO algorithm are effective, providing reliable site-selection references for subsequent urban microgreen space distributions.

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Location Decision of Logistics Distribution Centers Based on Artificial Neural Network

Cited by 3 publications

References 15 publications

Comprehensive Evaluation Model for Resettlement Site Selection in Karst Areas Using Multiobjective Particle Swarm Optimization

Comprehensive Evaluation Model for Resettlement Site Selection in Karst Areas Using Multiobjective Particle Swarm Optimization

Urban cold-chain logistics demand predicting model based on improved neural network model

Optimization of an Urban Microgreen Space Distribution Based on the PS-ACO Algorithm: A Case Study of Shenyang, China

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